
import pandas as pd
import json

import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go


import sys
import importlib

sys.path.insert(0, '../')
import general_utils as gen_ut

sys.path.insert(0, '../3_Hashtag_study')
import hashtag_util as ut_ht

sys.path.insert(0, '../4_URL_study')
import url_util as ut_url


with open('../1_Dataset_preparation/listControlledUsers.json','r') as file_object:  
      data = json.load(file_object)  

listSuspect = data['Novax']


def df_preparation(cols,filename='../tweets.csv'):
    columns = list(set(cols + ['id','user_screen_name','rt_user_screen_name','in_reply_to_screen_name']))
    
    df = pd.read_csv(filename,low_memory=False, usecols=columns)
    dfSuspect = pd.DataFrame()
    
    for sus in listSuspect:
        df1 = df.loc[df['user_screen_name'] == sus,:]
        dfSuspect = pd.concat([df1,dfSuspect],ignore_index=True)
        
        df1 = df.loc[df['rt_user_screen_name'] == sus,:]
        dfSuspect = pd.concat([df1,dfSuspect],ignore_index=True)
        
        df1 = df.loc[df['in_reply_to_screen_name'] == sus,:]
        dfSuspect = pd.concat([df1,dfSuspect],ignore_index=True)
        del df1
        
    dfSuspect.drop_duplicates(subset=['id'])
    return dfSuspect


df = df_preparation(['urls','created_at'])
df['created_at'] = pd.to_datetime(df['created_at'], format="%a %b %d %X %z %Y")
df


dfUrls = ut_url.df_url_counter(df)
dfUrls = ut_url.url_credibility('../1_Dataset_preparation/data/credibility.csv',dfUrls)
dfUrls


sum(dfUrls['count'])

76991


n = 20
fig = px.histogram(dfUrls.head(n),y=dfUrls.head(n).index,x='count',
            title="The most %d url used in the tweets"% n,orientation = 'h',color='Class')
fig.update_yaxes(title='URL name')
fig.update_yaxes(categoryorder='total descending')
fig.show()


#Creation of a dictionary of num of use per date
my_dict = {"url":[],"date":[], "count":[]};
for i in range(len(df)):
    s = df.loc[i,'urls']
    d = df.loc[i,'created_at']
    url = gen_ut.get_string_json(s,'display_url')
    if url:
        url = url[0].split("//")
        url = url[0].split("/")
        
        my_dict["url"].append(url[0])
        my_dict["date"].append(d)
        my_dict["count"].append(1)

dfUseUrl = pd.DataFrame.from_dict(my_dict)
dfUseUrl['Week/Year'] = dfUseUrl['date'].apply(lambda x: "%d-%d" % (x.isocalendar()[1] , x.isocalendar()[0]))
dfUseUrl.drop(['date'], axis=1,inplace=True)

dfUseUrl = dfUseUrl.groupby(['Week/Year', 'url']).sum()
dfUseUrl.reset_index(inplace=True)

dfUseUrl['Week/Year'] =pd.to_datetime(dfUseUrl['Week/Year']+ '-1', format="%W-%Y-%w")
dfUseUrl.sort_values(['Week/Year'],axis = 0,inplace=True,ascending=True)

dfUseUrl


#All in the same graphic
fig = go.Figure()

for w in dfUrls.head(10).index:
    mask = dfUseUrl['url'] == w
    fig.add_trace(go.Scatter(x=dfUseUrl.loc[mask,'Week/Year'], y=dfUseUrl.loc[mask,'count'],
                    mode='lines+markers',
                    name=w))
fig.update_layout(title='All url history use',xaxis_title='Date',yaxis_title='use count')

fig.show()


# All in different graphic
for w in dfUrls.head(10).index:
    fig = go.Figure()
    mask = dfUseUrl['url'] == w
    fig.add_trace(go.Scatter(x=dfUseUrl.loc[mask,'Week/Year'], y=dfUseUrl.loc[mask,'count'],
                    mode='lines+markers',
                    name=w))
    fig.update_layout(title="History use of url '%s'"%w,xaxis_title='Date',yaxis_title='use count')

    fig.show()


#Creating a map of all urls about youtube with the number of uses
listUrls = []
for s in df['urls']:
    urls = gen_ut.get_string_json(s,'display_url')
    for url in urls:
        if "youtu.be" in url or "youtube.com" in url:
            if url:
                #url = url.split("//")
                #url = url[0].split("/")
                listUrls.append(url)


dfUrlsYt = pd.DataFrame()
dfUrlsYt['url'] = listUrls
dfUrlsYt['count'] = 1


dfUrlsYt = dfUrlsYt.groupby('url').sum()
dfUrlsYt.sort_values(['count'], axis = 0,inplace=True,ascending=False)

dfUrlsYt


n = 20
fig = px.histogram(dfUrlsYt.head(n),y=dfUrlsYt.head(n).index,x='count',
            title="The most %d url about youtube used in the tweets"% n,orientation = 'h')
fig.update_yaxes(title='URL name')
fig.show()


from urllib.request import urlopen
'''
for i,c in dfUrlsYt.iterrows():
    #if "watch" not in i:
    print("http://"+i,"\t",c['count'])
    urlopen("http://"+i).read()'''

'\nfor i,c in dfUrlsYt.iterrows():\n    #if "watch" not in i:\n    print("http://"+i,"\t",c[\'count\'])\n    urlopen("http://"+i).read()'


#Creation of a dictionary of num of use per date
my_dict = {"url":[],"date":[], "count":[]};
for i in range(len(df)):
    s = df.loc[i,'urls']
    d = df.loc[i,'created_at']
    urls = gen_ut.get_string_json(s,'display_url')
    for url in urls:
        if "youtu.be" in url or "youtube.com" in url:
            if url:
                #url = url.split("//")
                #url = url[0].split("/")
                listUrls.append(url)
        
            my_dict["url"].append(url)
            my_dict["date"].append(d)
            my_dict["count"].append(1)

dfUseUrlYt = pd.DataFrame.from_dict(my_dict)
dfUseUrlYt['Week/Year'] = dfUseUrlYt['date'].apply(lambda x: "%d-%d" % (x.isocalendar()[1] , x.isocalendar()[0]))
dfUseUrlYt.drop(['date'], axis=1,inplace=True)

dfUseUrlYt = dfUseUrlYt.groupby(['Week/Year', 'url']).sum()
dfUseUrlYt.reset_index(inplace=True)

dfUseUrlYt['Week/Year'] =pd.to_datetime(dfUseUrlYt['Week/Year']+ '-1', format="%W-%Y-%w")
dfUseUrlYt.sort_values(['Week/Year'],axis = 0,inplace=True,ascending=True)

dfUseUrlYt


#All in the same graphic
fig = go.Figure()

for w in dfUrlsYt.head().index:
    mask = dfUseUrlYt['url'] == w
    fig.add_trace(go.Scatter(x=dfUseUrlYt.loc[mask,'Week/Year'], y=dfUseUrlYt.loc[mask,'count'],
                    mode='lines+markers',
                    name=w))
fig.update_layout(title='All url history use',xaxis_title='Date',yaxis_title='use count')

fig.show()


# All in different graphic
for w in dfUrlsYt.head().index:
    fig = go.Figure()
    mask = dfUseUrlYt['url'] == w
    fig.add_trace(go.Scatter(x=dfUseUrlYt.loc[mask,'Week/Year'], y=dfUseUrlYt.loc[mask,'count'],
                    mode='lines+markers',
                    name=w))
    fig.update_layout(title="History use of url '%s'"%w,xaxis_title='Date',yaxis_title='use count')

    fig.show()


mask = []
for url in df['urls']:
    mask.append("youtu.be/16PvXtfdmX0" in url)
df[mask]


df = df_preparation(['hashtags','created_at'])
df['created_at'] = pd.to_datetime(df['created_at'], format="%a %b %d %X %z %Y")


#Creating a map of all hashtags with the number of uses
listHashtags = []

for s in df['hashtags']:    
    [ listHashtags.append(x) for x in gen_ut.get_string_json(s,'text') ]
    
dfHashtags = pd.DataFrame()
dfHashtags['hashtags'] = listHashtags
dfHashtags['count'] = 0

dfHashtags = dfHashtags.groupby('hashtags').count()
dfHashtags.sort_values(['count'],axis = 0,inplace=True,ascending=False)

dfHashtags


dfUse = ut_ht.process_dfUse(df)
dfUse


ut_ht.visual_histogram(dfHashtags,150)


hastagRemove = ['vaccin.*','covid.*','corona.*','astrazeneca','pfizer','sarscov2','sputnikv','moderna']
dfHashtagFiltered = dfHashtags
for r in hastagRemove:
    mask = dfHashtagFiltered.index.str.lower().str.match(r) == True
    dfHashtagFiltered.drop(dfHashtagFiltered[mask].index, inplace=True)
    
dfMoreFiltered = dfHashtagFiltered
hastagRemove = ['.*lombardia.*','draghi','conte','m5s','mattarella','salvini','speranza','renzi','lega','.*governo.*',
           '.*moratti.*','zingaretti','scanzi','burioni','crisanti']
for r in hastagRemove:
    mask = dfMoreFiltered.index.str.lower().str.match(r) == True
    dfMoreFiltered.drop(dfMoreFiltered[mask].index, inplace=True)

ut_ht.visual_histogram(dfMoreFiltered,100)


ut_ht.visual_by_date_together(dfMoreFiltered,dfUse)


ut_ht.visual_by_date_split(dfMoreFiltered,dfUse)


df = df_preparation(['is_self_rt'])


dfSelf = df.groupby('user_screen_name').sum()
dfSelf['all_rt'] = df.groupby('user_screen_name').count().iloc[:,0]
dfSelf.sort_values(['all_rt'],inplace=True)
dfSelf


n = 20
fig = make_subplots(rows=1, cols=1)

fig.add_trace(go.Bar(y=dfSelf.tail(n).index, x=dfSelf.tail(n)['all_rt'],orientation='h',  name = 'All retweet'), row=1, col=1)
fig.add_trace(go.Bar(y=dfSelf.tail(n).index, x=dfSelf.tail(n)['is_self_rt'],orientation='h', name = 'Self retweet'), row=1, col=1)

fig.update_layout(title="How many retweet are self retweet (the most 20 retweeter)")
fig.update_xaxes(title="Count of retweets")
fig.update_yaxes(title="Username")

fig.update_layout(
    barmode="overlay",
    bargap=0.1)


fig.show()


df = df_preparation(['is_self_rt'])
df


dfRetweet = df.dropna(subset=['rt_user_screen_name']).copy()
dfRetweet.drop(columns=['in_reply_to_screen_name'],inplace=True,errors='ignore')
dfRetweet = dfRetweet.groupby('rt_user_screen_name').count()

dfRetweet.rename(columns={'user_screen_name':'all_rt'},inplace=True,errors='ignore')
dfRetweet['self_rt'] = df.dropna(subset=['rt_user_screen_name'
                                        ]).copy().groupby('rt_user_screen_name').sum().loc[:,'is_self_rt']
dfRetweet.drop(columns=['is_self_rt'],inplace=True,errors='ignore')

dfRetweet['real_rt'] = dfRetweet['all_rt'] - dfRetweet['self_rt']
dfRetweet.sort_values('real_rt',ascending=False,inplace=True)
dfRetweet


n = 20
fig =px.histogram(dfRetweet.head(n),y=dfRetweet.head(n).index,x='real_rt',orientation='h')
fig.update_yaxes(title='username')
fig.update_layout(title="The most %d users retweeted (without self retweet)"%n)
fig.show()
sum(dfRetweet['real_rt'])

380325


dfReply = df.dropna(subset=['in_reply_to_screen_name']).copy()
dfReply.drop(columns=['rt_user_screen_name'],inplace=True,errors='ignore')
dfReply.drop(columns=['is_self_rt'],inplace=True,errors='ignore')

dfReply = dfReply.groupby('in_reply_to_screen_name').count()

dfReply.rename(columns={'user_screen_name':'count'},inplace=True,errors='ignore')
dfReply.sort_values('count',ascending=False,inplace=True)

dfReply


n = 20
fig =px.histogram(dfReply.head(n),y=dfReply.head(n).index,x='count',orientation='h')
fig.update_yaxes(title='username')
fig.update_layout(title="The most %d users replied"%n)
fig.show()


df = df_preparation(['is_self_rt'])
df1 = df.copy()

df1['sum_total_posts'] = 1
df1 = df1.groupby('user_screen_name').sum()

df = df.groupby('user_screen_name').count()
df['sum_total_posts'] = df1['sum_total_posts']
df['sum_self_rt'] = df1['is_self_rt']
del df1

df.rename(columns={'in_reply_to_screen_name':'num_reply','rt_user_screen_name':'num_rt'},inplace=True,errors='ignore')

df['num_reply'] = dfReply
df.loc[df['num_reply'].isna(),'num_reply'] = 0

df['num_rt'] = dfRetweet['real_rt']
df.loc[df['num_rt'].isna(),'num_rt'] = 0

df


df.describe()


df.sort_values('num_rt',ascending=False,inplace=True)
df = df.head(10)
df


n = 20
fig = make_subplots(rows=1, cols=1)

fig.add_trace(go.Bar(y=df.head(n).index, x=df.head(n)['num_reply'],orientation='h',  name = 'Reply'), row=1, col=1)
fig.add_trace(go.Bar(y=df.head(n).index, x=df.head(n)['num_rt']-df.head(n)['sum_self_rt'],orientation='h', name = 'Retweet'), row=1, col=1)
fig.add_trace(go.Bar(y=df.head(n).index, x=df.head(n)['sum_self_rt'],orientation='h',  name = 'Self retweet'), row=1, col=1)

fig.update_layout(title="How many time this users are retweeted")
fig.update_xaxes(title="Count")
fig.update_yaxes(title="Username")
fig.show()


for i in range(0,len(df),2):
    fig = make_subplots(rows=1,cols=2,specs=[[{'type':'domain'}, {'type':'domain'}]])
    names = ['Reply', 'Retweet','Tweet','Self retweet']
    
    u = df.index[i]
    if u in listSuspect:
        values = [df.loc[u,'num_reply'], df.loc[u,'num_rt'] - df.loc[u,'sum_self_rt'],
                  df.loc[u,'sum_total_posts']-(df.loc[u,'num_reply']+df.loc[u,'num_rt']),
                 df.loc[u,'sum_self_rt']]
        fig.add_trace(go.Pie(labels = names, values = values,title="%s"%u,textposition='inside')
                      ,row=1,col=1)


        u = df.index[i+1]
        values = [df.loc[u,'num_reply'], df.loc[u,'num_rt'] - df.loc[u,'sum_self_rt'],
                  df.loc[u,'sum_total_posts']-(df.loc[u,'num_reply']+df.loc[u,'num_rt']),
                df.loc[u,'sum_self_rt']]
        fig.add_trace(go.Pie(labels = names, values = values,title="%s"%u,textposition='inside')
                      ,row=1,col=2)

        fig.update_layout(title="Post type division")

        fig.update_traces(marker=dict(colors=['red', 'blue', 'green','cyan']))


        fig.show()

	id	created_at	user_screen_name	in_reply_to_screen_name	rt_user_screen_name	urls
0	1315035428098015237	2020-10-10 21:04:12+00:00	KurtRoll3	borghi_claudio	NaN	[]
1	1314915759932534788	2020-10-10 13:08:41+00:00	Giovanni1946861	borghi_claudio	NaN	[]
2	1314824033154871296	2020-10-10 07:04:12+00:00	Franco_Barile1	borghi_claudio	NaN	[]
3	1315421329072545793	2020-10-11 22:37:38+00:00	AnnamariaCorra7	borghi_claudio	NaN	[]
4	1315366608202805248	2020-10-11 19:00:12+00:00	gaetanovetri1	borghi_claudio	NaN	[]
...	...	...	...	...	...	...
442502	1394545421578604548	2021-05-18 06:48:32+00:00	IacobellisT	NaN	NaN	[]
442503	1394545570417725446	2021-05-18 06:49:08+00:00	IacobellisT	NaN	rubino7004	[]
442504	1394545509868707841	2021-05-18 06:48:53+00:00	IacobellisT	NaN	GiuseppePalma78	[]
442505	1394545510346895363	2021-05-18 06:48:53+00:00	IacobellisT	NaN	byoblu	[]
442506	1394545697526067201	2021-05-18 06:49:38+00:00	IacobellisT	NaN	valy_s	[]

	count	Class
url
twitter.com	15416	none
youtube.com	5114	none
imolaoggi.it	3436	low
iltempo.it	1336	high
ansa.it	1297	high
...	...	...
dailyhealthindustry.it	1	none
dailymirror.lk	1	none
dailypostusa.com	1	none
dailystar.co.uk	1	low
…xevxq4s5y-1scandal-com.translate.goog	1	none

	Week/Year	url	count
0	2020-01-06	ANSA.it	1
14	2020-01-06	youtu.be	1
12	2020-01-06	twitter.com	14
11	2020-01-06	torino.repubblica.it	1
10	2020-01-06	threader.app	1
...	...	...	...
2774	2021-05-17	ilrestodelcarlino.it	2
2775	2021-05-17	ilsimplicissimus2.com	3
2776	2021-05-17	ilsole24ore.com	2
2760	2021-05-17	genova24.it	2
2861	2021-05-17	vcomevittoria.it	74

	count
url
youtube.com/watch?v=U-kC9X…	472
youtube.com/watch?v=xgfQfD…	346
youtube.com/watch?v=-BbnIl…	276
youtube.com/watch?v=q04zOg…	155
youtube.com/watch?v=GzXbsu…	141
...	...
youtu.be/e1R7l28JJeo	1
youtu.be/eK9A3VjaBaU	1
youtu.be/eqvFlgydWVw	1
youtube.com/watch?v=R2ok5T…	1
m.youtube.com/watch?v=haxx_B…	1

	Week/Year	url	count
0	2020-01-06	youtu.be/WWcwInBEdGw	1
549	2020-01-20	youtu.be/JUKI1CEs7Lw	1
550	2020-01-20	youtu.be/JfDIie01QeE	1
551	2020-01-20	youtu.be/K9-PUInmA-I	6
552	2020-01-20	youtube.com/watch?v=UQpaNP…	1
...	...	...	...
436	2021-05-17	youtu.be/Fck6ZwNiq-w	7
435	2021-05-17	youtu.be/ERuI4arMu4c	2
434	2021-05-17	youtu.be/7k283DjMinw	1
445	2021-05-17	youtu.be/tdJoMIAZ334	1
440	2021-05-17	youtu.be/_1KsDNQWrgU	3

Account iteration study (supect users)¶

Import¶

Function definition¶

Link study¶

Use of different Youtube urls¶

Most shared video avable (here)¶

Hahstag study¶

Self retweet¶

Iteration with other users¶

Retweet¶

Reply¶

Suspect iteration study¶

	id	created_at	user_screen_name	in_reply_to_screen_name	rt_user_screen_name	urls
312066	1251092982167678976	2020-04-17 10:19:45+00:00	Samira1577	NaN	MinervaMcGrani1	[{'url': 'https://t.co/eAnhBta27L', 'expanded_...
348633	1251065679660437504	2020-04-17 08:31:16+00:00	gael99	NaN	MinervaMcGrani1	[{'url': 'https://t.co/eAnhBta27L', 'expanded_...
368741	1251285545101201414	2020-04-17 23:04:56+00:00	Lara_Eureka	NaN	MinervaMcGrani1	[{'url': 'https://t.co/eAnhBta27L', 'expanded_...
368766	1251280724264615937	2020-04-17 22:45:46+00:00	chiara_m80	NaN	MinervaMcGrani1	[{'url': 'https://t.co/eAnhBta27L', 'expanded_...
368825	1251267346485960705	2020-04-17 21:52:37+00:00	Esticazzi1965	NaN	MinervaMcGrani1	[{'url': 'https://t.co/eAnhBta27L', 'expanded_...
...	...	...	...	...	...	...
373542	1288712339114602497	2020-07-30 05:45:39+00:00	VittorioFerram1	NaN	MinervaMcGrani1	[{'url': 'https://t.co/eAnhBta27L', 'expanded_...
390609	1251091137995161601	2020-04-17 10:12:26+00:00	MinervaMcGrani1	NaN	MinervaMcGrani1	[{'url': 'https://t.co/eAnhBta27L', 'expanded_...
390611	1251063946695639040	2020-04-17 08:24:23+00:00	MinervaMcGrani1	NaN	NaN	[{'url': 'https://t.co/eAnhBta27L', 'expanded_...
419639	1251097730287575040	2020-04-17 10:38:37+00:00	Z3r0Rules	NaN	MinervaMcGrani1	[{'url': 'https://t.co/eAnhBta27L', 'expanded_...
425808	1251238499245850626	2020-04-17 19:57:59+00:00	xenonian1	NaN	MinervaMcGrani1	[{'url': 'https://t.co/eAnhBta27L', 'expanded_...

	count
hashtags
vaccino	9830
COVID19	9506
Covid19	9393
vaccini	6177
Pfizer	4976
...	...
SiamoTroppi	1
Sidney	1
SilvioBerlusconi	1
SilvioGarattini	1
𝐕𝐀𝐂𝐂𝐈𝐍𝐈	1

	Week/Year	hashtag	count
0	2020-01-06	AIFA	3
8660	2020-01-06	EmiliaRomagna	1
8659	2020-01-06	Burioni	1
8658	2020-01-06	Bonaccini	1
8657	2020-01-06	Bibbiano	1
...	...	...	...
19005	2021-05-17	Pfizer	6
19004	2021-05-17	Norimberga2	1
19003	2021-05-17	NoObbligoVaccinale	1
19017	2021-05-17	VacciNazismo	1
10910	2021-05-17	virologi	1

	id	is_self_rt	all_rt
user_screen_name
SpaakMr	1.378293e+18	0	1
Samsung9900	1.389977e+18	0	1
Samuel21091422	1.373232e+18	0	1
remodalfonso	1.293633e+18	0	1
SanMichele33	1.353708e+18	0	1
...	...	...	...
MarySpes	6.301979e+21	0	4648
xenonian1	6.688016e+21	24	5017
Z3r0Rules	7.216171e+21	392	5347
Pietro_Otto	7.410851e+21	0	5412
Piero42395724	7.962673e+21	0	5835

	id	all_rt	self_rt	real_rt
rt_user_screen_name
MinervaMcGrani1	28977	28977	0	28977
BarbaraRaval	21752	21752	70	21682
valy_s	21626	21626	2	21624
noitre32	18133	18133	0	18133
intuslegens	13617	13617	0	13617
...	...	...	...	...
Salvato00506710	1	1	0	1
Salviniunico	1	1	0	1
SalvoInUK	1	1	0	1
SamaRosa70	1	1	0	1
zuhura05	1	1	0	1

	id	count
in_reply_to_screen_name
borghi_claudio	4564	4564
valy_s	2973	2973
MinervaMcGrani1	1887	1887
cris_cersei	1758	1758
ladyonorato	1628	1628
...	...	...
NotKayloss	1	1
NostraD64669142	1	1
NormalitaNuova	1	1
Norbertus1860	1	1
zucconideputato	1	1

	id	num_reply	num_rt	is_self_rt	sum_total_posts	sum_self_rt
user_screen_name
00000o0OOOO00	15	0.0	1.0	15	15.0	0
00650301	1	0.0	0.0	1	1.0	0
00_Maura	1	0.0	0.0	1	1.0	0
0109Taiyo	1	0.0	0.0	1	1.0	0
01nikks	3	0.0	0.0	3	3.0	0
...	...	...	...	...	...	...
zucche_rosa	1	0.0	0.0	1	1.0	0
zucchi1911	1	0.0	0.0	1	1.0	0
zucco52	2	0.0	0.0	2	2.0	0
zxuz53	3	0.0	0.0	3	3.0	0
zziocane66	116	0.0	0.0	116	116.0	0

	id	num_reply	num_rt	is_self_rt	sum_total_posts	sum_self_rt
count	24743.000000	24743.00000	24743.000000	24743.000000	24743.000000	24743.000000
mean	17.884129	1.57196	15.070606	17.884129	17.884129	0.055208
std	140.411696	48.21337	410.179670	140.411696	140.411696	3.410107
min	1.000000	0.00000	0.000000	1.000000	1.000000	0.000000
25%	1.000000	0.00000	0.000000	1.000000	1.000000	0.000000
50%	2.000000	0.00000	0.000000	2.000000	2.000000	0.000000
75%	7.000000	0.00000	0.000000	7.000000	7.000000	0.000000
max	5835.000000	4564.00000	28977.000000	5835.000000	5835.000000	392.000000

	id	num_reply	num_rt	is_self_rt	sum_total_posts	sum_self_rt
user_screen_name
MinervaMcGrani1	4176	1887.0	28977.0	4176	4176.0	0
BarbaraRaval	1371	841.0	21682.0	1371	1371.0	70
valy_s	3064	2973.0	21624.0	3064	3064.0	2
noitre32	2045	1290.0	18133.0	2045	2045.0	0
intuslegens	60	450.0	13617.0	60	60.0	0
borghi_claudio	392	4564.0	13222.0	392	392.0	162
pbecchi	176	1102.0	12736.0	176	176.0	16
miia_2018	1075	395.0	12375.0	1075	1075.0	0
cris_cersei	852	1758.0	11526.0	852	852.0	8
ladyonorato	507	1628.0	11410.0	507	507.0	4